Optimizing the synthesis and purification of MS2 virus like particles
Bibliographic record
Abstract
Introducing bacteriophage MS2 virus-like particles (VLPs) as gene and drug delivery tools increases the demand for optimizing their production and purification procedure. PEG precipitation method is used efficiently to purify VLPs, while the effects of pH and different electrolytes on the stability, size, and homogeneity of purified MS2 VLPs, and the encapsulated RNA sequences remained to be elucidated. In this regard, a vector, capable of producing VLP with an shRNA packed inside was prepared. The resulting VLPs in different buffers/solutions were assessed for their size, polydispersity index, and ability to protect the enclosed shRNA. We report that among Tris, HEPES, and PBS, with or without NaNO3, and also NaNO3 alone in different pH and ionic concentrations, the 100 mM NaNO3-Tris buffer with pH:8 can be used as a new and optimal MS2 VLP production buffer, capable of inhibiting the VLPs aggregation. These VLPs show a size range of 27-30 nm and suitable homogeneity with minimum 12-month stability at 4 °C. Moreover, the resulting MS2 VLPs were highly efficient and stable for at least 48 h in conditions similar to in vivo. These features of MS2 VLPs produced in the newly introduced buffer make them an appropriate candidate for therapeutic agents' delivery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".